## Scripts to Train and Evaluate Chat Models

### Fine-tuning

In the handbook, we provide three main ways to align LLMs for chat:

- Full fine-tuning on a multi-GPU machine with DeepSpeed ZeRO-3 (tested on an 8 x A100 (80GB) node).
- LoRA fine-tuning on a single consumer 24GB GPU (tested on a RTX 4090).
- LoRA fine-tuning on a multi-GPU machine with DeepSpeed ZeRO-3 (tested on a 2 x A100s (80GB)).

In practice, we find comparable performance for both full and LoRA fine-tuning, with the latter having the advantage of producing small adapter weights that are fast to upload and download from the Hugging Face Hub. Here's the two general commands to fine-tune your models:

```shell
# Full training with ZeRO-3 on 8 GPUs
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_{task}.py recipes/{model_name}/{task}/config_full.yaml

# LoRA training on a single GPU
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/multi_gpu.yaml --num_processes=1 scripts/run_{task}.py recipes/{model_name}/{task}/config_lora.yaml

# LoRA training with ZeRO-3 on two or more GPUs
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml --num_processes={num_gpus} scripts/run_{task}.py recipes/{model_name}/{task}/config_lora.yaml
```

Here `{task}` refers to type of training you wish to run (SFT, DPO, etc), while `{model_name}` refers to the choice of recipe in the `recipes/` directory. For example, to replicate Zephyr 7B you can run:

```shell
# Step 1 - train SFT policy
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_sft.py recipes/zephyr-7b/sft/config_full.yaml

# Step 2 - align with DPO
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_dpo.py recipes/zephyr-7b/dpo/config_full.yaml
```

By default, these scripts will push each model to your Hugging Face Hub username, i.e. `{username}/{model_name}-{task}`. You can override the parameters in each YAML config by appending them to the command as follows:

```shell
# Change batch size, number of epochs etc
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_{task}.py recipes/{model_name}/{task}/config_full.yaml --per_device_train_batch_size=42 --num_train_epochs=5
```

By default all training metrics are logged with TensorBoard. If you have a [Weights and Biases](https://wandb.ai/site) account and are logged in, you can view the training metrics by appending `--report_to=wandb`, e.g.

```shell
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml scripts/run_{task}.py recipes/{model_name}/{task}/config_full.yaml --report_to=wandb
```

### Launching jobs on a Slurm cluster

If you have access to a Slurm cluster, we provide a `recipes/launch.slurm` script that will automatically queue training jobs for you. Here's how you can use it:

```shell
sbatch --job-name=handbook_{task} --nodes=1 recipes/launch.slurm {model_name} {task} {precision} {accelerator}
```

Here `{model_name}` and `{task}` are defined as above, while `{precision}` refers to the type of training (full vs LoRA) and `{accelerator}` refers to the choice of 🤗 Accelerate config in `recipes/accelerate_configs`. Here's a concrete example to run SFT on 1 node of 8 GPUs:

```shell
sbatch --job-name=handbook_sft --nodes=1 recipes/launch.slurm zephyr-7b sft full deepspeed_zero3
```

You can scale the number of nodes by increasing the `--nodes` flag; in these cases we recommend also scaling up the per-device batch size or number of gradient accumulation steps to keep the global batch size constant (and thus replicate our results).

**Note:** the configuration in `recipes/launch.slurm` is optimised for the Hugging Face Compute Cluster and may require tweaking to be adapted to your own compute nodes.